Compare model versions

Diff

Before

license: apache-2.0
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen3-VL-8B-Instruct

After

base_model: Qwen/Qwen3-VL-8B-Instruct
base_model_relation: quantized
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text

Diff

Before

<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>

After

<div align="left">
  <a href="https://cyan.kiwi" target="_blank"><img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-banners-cyankiwi.png" width="800" alt="cyankiwi, the LLM inference agent for open models"></a>
</div>

<div align="left">
  <table style="border-collapse:collapse; border:none;">
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Website</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://cyan.kiwi/" target="_blank">cyan.kiwi</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Models</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/cyankiwi" target="_blank">Hugging Face</a> · <a href="https://www.modelscope.ai/organization/cyankiwi" target="_blank">ModelScope</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Connect</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://x.com/cyankiwi_ai" target="_blank">X</a> · <a href="https://www.reddit.com/r/cyankiwi/" target="_blank">Reddit</a> · <a href="mailto:ton@cyan.kiwi">Email</a></td>
    </tr>
  </table>
</div>

<br>

<div align="left">
  <img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-banners-awq.png" width="800" alt="cyankiwi Activation-aware Weight Quantization">
</div>

<div align="left">
  <table style="border-collapse:collapse; border:none;">
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Version</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://github.com/vllm-project/llm-compressor" target="_blank">llm-compressor</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Calibration</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset" target="_blank">nvidia/Llama-Nemotron-Post-Training-Dataset</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Model Size</b></td>
      <td align="left" style="border:none; padding:4px 0;">7.55 GB</td>
    </tr>
  </table>
</div>

<br>

<p align="left">
  <a href="https://cyan.kiwi/workspace?prompt=Deploy%20cyankiwi/Qwen3-VL-8B-Instruct-AWQ-4bit" target="_blank"><img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-button-deploy.png" width="333" alt="Deploy on cyan.kiwi private endpoints"></a>
</p>

---

<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>

Diff

Before

Here we show a code snippet to show how to use the chat model with `transformers`:

```python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor

# default: Load the model on the available device(s)
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct", dtype="auto", device_map="auto"
)

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen3VLForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen3-VL-8B-Instruct",
#     dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

processor = AutoProcessor.from_pretrained("Qwen/Qwen/Qwen3-VL-8B-Instruct")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```

After

Here we show a code snippet to show how to use the chat model with `transformers`:

```python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor

# default: Load the model on the available device(s)
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen3-VL-8B-Instruct", dtype="auto", device_map="auto"
)

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen3VLForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen3-VL-8B-Instruct",
#     dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-8B-Instruct")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```